Goal¶

This challenge is for an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this project is to develop a machine learning model to predict the likelihood of each customer “churning,” i.e. becoming inactive and not making any transactions for 90 days.¶

This solution will help this telecom company to better serve their customers by understanding which customers are at risk of leaving.¶

Variable names and definitions¶

REGION - The location of each client¶

TENURE - duration in the network¶

MONTANT - top-up amount¶

FREQUENCE_RECH - number of times the customer refilled¶

REVENUE - client monthly income of each client¶

ARPU_SEGMENT - income over 90 days / 3¶

FREQUENCE - number of times the client has made an income¶

DATA_VOLUME - number of connections¶

ON_NET - inter expresso call¶

ORANGE - call to orange¶

TIGO - call to Tigo¶

ZONE1 - call to zones1¶

ZONE2 - call to zones2¶

MRG - a client who is going¶

REGULARITY - number of times the client is active for 90 days¶

TOP_PACK - the most active packs¶

FREQ_TOP_PACK - top pack number of times the client has activated the top pack packages¶

CHURN- Target variable to predict¶

Questions¶

1. Are customers with longer tenures with the company are less likely to churn¶

2. Do customers with more active days churn less than those less active days?¶

3. Do customers who earn more income churn less than those who earn less?¶

Hypothesis¶

1. Customers who stay longer with the Telco churn less than others who stay less.¶

In [1]:
## Importing relevant Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
!pip install seaborn 
import seaborn as sns
import plotly.graph_objects as go
import warnings
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_log_error
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import *
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import fbeta_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
from imblearn.over_sampling import SMOTE
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import *
import plotly.express as px
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer

#profiling
import pandas_profiling
from pandas_profiling import ProfileReport

Requirement already satisfied: seaborn in ./opt/anaconda3/lib/python3.9/site-packages (0.11.2)
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Requirement already satisfied: scipy>=1.0 in ./opt/anaconda3/lib/python3.9/site-packages (from seaborn) (1.7.3)
Requirement already satisfied: matplotlib>=2.2 in ./opt/anaconda3/lib/python3.9/site-packages (from seaborn) (3.5.1)
Requirement already satisfied: pandas>=0.23 in ./opt/anaconda3/lib/python3.9/site-packages (from seaborn) (1.4.2)
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Requirement already satisfied: cycler>=0.10 in ./opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)
Requirement already satisfied: packaging>=20.0 in ./opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (21.3)
Requirement already satisfied: pillow>=6.2.0 in ./opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (9.0.1)
Requirement already satisfied: pytz>=2020.1 in ./opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->seaborn) (2021.3)
Requirement already satisfied: six>=1.5 in ./opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.12.0)
In [2]:
!pip install --upgrade pip

Requirement already satisfied: pip in ./opt/anaconda3/lib/python3.9/site-packages (23.0.1)
In [3]:
#Loading datasets
Train = pd.read_csv('/Users/Admin/Desktop/Churn Capstone/Train.csv')
Test = pd.read_csv('/Users/Admin/Desktop/Churn Capstone/Test.csv')
In [4]:
Train.head()
Out[4]:
user_id REGION TENURE MONTANT FREQUENCE_RECH REVENUE ARPU_SEGMENT FREQUENCE DATA_VOLUME ON_NET ORANGE TIGO ZONE1 ZONE2 MRG REGULARITY TOP_PACK FREQ_TOP_PACK CHURN
0 7ee9e11e342e27c70455960acc80d3f91c1286d1 DAKAR K > 24 month 20000.0 47.0 21602.0 7201.0 52.0 8835.0 3391.0 396.0 185.0 NaN NaN NO 62 On net 200F=Unlimited _call24H 30.0 0
1 50443f42bdc92b10388fc56e520e4421a5fa655c NaN K > 24 month NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NO 3 NaN NaN 0
2 da90b5c1a9b204c186079f89969aa01cb03c91b2 NaN K > 24 month NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NO 1 NaN NaN 0
3 364ec1b424cdc64c25441a444a16930289a0051e SAINT-LOUIS K > 24 month 7900.0 19.0 7896.0 2632.0 25.0 9385.0 27.0 46.0 20.0 NaN 2.0 NO 61 Data:490F=1GB,7d 7.0 0
4 d5a5247005bc6d41d3d99f4ef312ebb5f640f2cb DAKAR K > 24 month 12350.0 21.0 12351.0 4117.0 29.0 9360.0 66.0 102.0 34.0 NaN NaN NO 56 All-net 500F=2000F;5d 11.0 0
In [5]:
Train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1077024 entries, 0 to 1077023
Data columns (total 19 columns):
 #   Column          Non-Null Count    Dtype  
---  ------          --------------    -----  
 0   user_id         1077024 non-null  object 
 1   REGION          652687 non-null   object 
 2   TENURE          1077024 non-null  object 
 3   MONTANT         699139 non-null   float64
 4   FREQUENCE_RECH  699139 non-null   float64
 5   REVENUE         714669 non-null   float64
 6   ARPU_SEGMENT    714669 non-null   float64
 7   FREQUENCE       714669 non-null   float64
 8   DATA_VOLUME     547261 non-null   float64
 9   ON_NET          683850 non-null   float64
 10  ORANGE          629880 non-null   float64
 11  TIGO            432250 non-null   float64
 12  ZONE1           84898 non-null    float64
 13  ZONE2           68794 non-null    float64
 14  MRG             1077024 non-null  object 
 15  REGULARITY      1077024 non-null  int64  
 16  TOP_PACK        626129 non-null   object 
 17  FREQ_TOP_PACK   626129 non-null   float64
 18  CHURN           1077024 non-null  int64  
dtypes: float64(12), int64(2), object(5)
memory usage: 156.1+ MB
In [6]:
Train.isnull().sum()
Out[6]:
user_id                 0
REGION             424337
TENURE                  0
MONTANT            377885
FREQUENCE_RECH     377885
REVENUE            362355
ARPU_SEGMENT       362355
FREQUENCE          362355
DATA_VOLUME        529763
ON_NET             393174
ORANGE             447144
TIGO               644774
ZONE1              992126
ZONE2             1008230
MRG                     0
REGULARITY              0
TOP_PACK           450895
FREQ_TOP_PACK      450895
CHURN                   0
dtype: int64
In [7]:
# Finding the percentage of missing values
Train_missing_cols = (round(((Train.isnull().sum()/len(Train.index))*100),2).to_frame('null')).sort_values('null', ascending=False)
Train_missing_cols
Out[7]:
null
ZONE2 93.61
ZONE1 92.12
TIGO 59.87
DATA_VOLUME 49.19
FREQ_TOP_PACK 41.86
TOP_PACK 41.86
ORANGE 41.52
REGION 39.40
ON_NET 36.51
MONTANT 35.09
FREQUENCE_RECH 35.09
REVENUE 33.64
ARPU_SEGMENT 33.64
FREQUENCE 33.64
REGULARITY 0.00
user_id 0.00
MRG 0.00
TENURE 0.00
CHURN 0.00
In [8]:
# Dropping columns with missing values above 50%
Train.drop(columns = ['ZONE2','ZONE1','TIGO','ORANGE','ON_NET','REGION','TOP_PACK', 'MRG', 'REVENUE'], inplace = True)
Train
Out[8]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
0 7ee9e11e342e27c70455960acc80d3f91c1286d1 K > 24 month 20000.0 47.0 7201.0 52.0 8835.0 62 30.0 0
1 50443f42bdc92b10388fc56e520e4421a5fa655c K > 24 month NaN NaN NaN NaN NaN 3 NaN 0
2 da90b5c1a9b204c186079f89969aa01cb03c91b2 K > 24 month NaN NaN NaN NaN NaN 1 NaN 0
3 364ec1b424cdc64c25441a444a16930289a0051e K > 24 month 7900.0 19.0 2632.0 25.0 9385.0 61 7.0 0
4 d5a5247005bc6d41d3d99f4ef312ebb5f640f2cb K > 24 month 12350.0 21.0 4117.0 29.0 9360.0 56 11.0 0
... ... ... ... ... ... ... ... ... ... ...
1077019 56e22fe3312a48cf860b043a021dab275383a20a K > 24 month NaN NaN NaN NaN NaN 16 NaN 0
1077020 587b72930e4d36b3fb94a18c3ef65c2079460eee K > 24 month 2500.0 5.0 833.0 5.0 0.0 34 2.0 0
1077021 d6831b8edacb7d9928b3f053fb1283574577ae42 K > 24 month NaN NaN NaN NaN NaN 3 NaN 1
1077022 cb1f6510b084173c0deae49095f35ac29f916701 K > 24 month 600.0 1.0 200.0 1.0 591.0 16 1.0 0
1077023 28f56fd0d9f8d8647bb6c62e7a3f4f35f49f4d6f K > 24 month 1500.0 4.0 500.0 5.0 1265.0 50 2.0 0

1077024 rows × 10 columns

In [9]:
Test.head()
Out[9]:
user_id REGION TENURE MONTANT FREQUENCE_RECH REVENUE ARPU_SEGMENT FREQUENCE DATA_VOLUME ON_NET ORANGE TIGO ZONE1 ZONE2 MRG REGULARITY TOP_PACK FREQ_TOP_PACK
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 MATAM I 18-21 month 2500.0 5.0 2500.0 833.0 5.0 0.0 64.0 70.0 NaN NaN NaN NO 35 All-net 500F=2000F;5d 5.0
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e NaN K > 24 month NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NO 2 NaN NaN
2 5a4db591c953a8d8f373877fad37aaf4268899a1 NaN K > 24 month NaN NaN NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NO 22 NaN NaN
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c NaN K > 24 month NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NO 6 NaN NaN
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df SAINT-LOUIS K > 24 month 5100.0 7.0 5637.0 1879.0 15.0 7783.0 30.0 24.0 0.0 0.0 NaN NO 60 Data:1000F=2GB,30d 4.0
In [10]:
Test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 190063 entries, 0 to 190062
Data columns (total 18 columns):
 #   Column          Non-Null Count   Dtype  
---  ------          --------------   -----  
 0   user_id         190063 non-null  object 
 1   REGION          115330 non-null  object 
 2   TENURE          190063 non-null  object 
 3   MONTANT         123695 non-null  float64
 4   FREQUENCE_RECH  123695 non-null  float64
 5   REVENUE         126422 non-null  float64
 6   ARPU_SEGMENT    126422 non-null  float64
 7   FREQUENCE       126422 non-null  float64
 8   DATA_VOLUME     96716 non-null   float64
 9   ON_NET          120771 non-null  float64
 10  ORANGE          111417 non-null  float64
 11  TIGO            76555 non-null   float64
 12  ZONE1           14850 non-null   float64
 13  ZONE2           12011 non-null   float64
 14  MRG             190063 non-null  object 
 15  REGULARITY      190063 non-null  int64  
 16  TOP_PACK        110773 non-null  object 
 17  FREQ_TOP_PACK   110773 non-null  float64
dtypes: float64(12), int64(1), object(5)
memory usage: 26.1+ MB
In [11]:
Test.drop(columns = ['ZONE2','ZONE1','TIGO','ORANGE','ON_NET','REGION','TOP_PACK','MRG', 'REVENUE'], inplace = True)
Test
Out[11]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 I 18-21 month 2500.0 5.0 833.0 5.0 0.0 35 5.0
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e K > 24 month NaN NaN NaN NaN NaN 2 NaN
2 5a4db591c953a8d8f373877fad37aaf4268899a1 K > 24 month NaN NaN NaN NaN 0.0 22 NaN
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c K > 24 month NaN NaN NaN NaN NaN 6 NaN
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df K > 24 month 5100.0 7.0 1879.0 15.0 7783.0 60 4.0
... ... ... ... ... ... ... ... ... ...
190058 1092956a3dad77ceb7e8d7c70e3e13f77b60e2aa F 9-12 month 500.0 1.0 169.0 1.0 0.0 14 1.0
190059 bec10becca7faa8e9cab9981b3aee5e9a7f04828 K > 24 month 2000.0 4.0 667.0 5.0 0.0 29 3.0
190060 7ac6fc191f8732b1b146e57f9ede983626b93eae K > 24 month NaN NaN NaN NaN NaN 2 NaN
190061 d592c81971d6120b0d19f9ace85f278ea21b89a0 K > 24 month 300.0 2.0 99.0 2.0 0.0 12 NaN
190062 9b4f57aeef72cd52c634498d0ea27ea3fbb67bf4 K > 24 month NaN NaN NaN NaN NaN 2 NaN

190063 rows × 9 columns

In [12]:
Train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1077024 entries, 0 to 1077023
Data columns (total 10 columns):
 #   Column          Non-Null Count    Dtype  
---  ------          --------------    -----  
 0   user_id         1077024 non-null  object 
 1   TENURE          1077024 non-null  object 
 2   MONTANT         699139 non-null   float64
 3   FREQUENCE_RECH  699139 non-null   float64
 4   ARPU_SEGMENT    714669 non-null   float64
 5   FREQUENCE       714669 non-null   float64
 6   DATA_VOLUME     547261 non-null   float64
 7   REGULARITY      1077024 non-null  int64  
 8   FREQ_TOP_PACK   626129 non-null   float64
 9   CHURN           1077024 non-null  int64  
dtypes: float64(6), int64(2), object(2)
memory usage: 82.2+ MB
In [13]:
Train.FREQUENCE_RECH.unique()
Out[13]:
array([ 47.,  nan,  19.,  21.,   2.,   1.,  13.,   3.,   5.,  14.,   7.,
        20.,  15.,   4.,  16.,  11.,   8.,  23.,  18.,  17.,  12.,  27.,
        50.,  40.,   9.,   6.,  32.,  39.,  33.,  38.,  48.,  60.,  26.,
        49.,  10.,  44.,  22.,  30.,  28.,  46.,  52.,  25.,  45.,  24.,
        36.,  62.,  37.,  58.,  29.,  69.,  51.,  31.,  64.,  35.,  56.,
        85.,  73.,  53.,  55.,  34.,  57.,  43.,  72.,  70.,  65.,  68.,
        61.,  75.,  41.,  42.,  54.,  76.,  80.,  71.,  83.,  82.,  81.,
        59.,  74.,  67.,  63.,  66.,  93.,  78.,  77.,  84.,  79.,  87.,
        88.,  92.,  94., 110.,  86.,  95.,  98.,  89.,  96., 101., 100.,
       114.,  91., 104.,  90., 103.,  97., 106., 109.,  99., 117., 113.,
       112., 121., 115., 108., 111., 119., 118., 102., 105., 131.])
In [14]:
Train.REGULARITY.unique()
Out[14]:
array([62,  3,  1, 61, 56,  6, 10,  2, 16, 43,  8, 18, 12, 24, 54, 29, 14,
       40, 23, 52, 60, 34, 13, 46, 47, 31,  9, 58, 38, 36, 19,  5, 39, 55,
       27,  4, 32, 26, 17, 48, 22, 53, 59, 20, 44,  7, 41, 49, 33, 42, 28,
       30, 15, 57, 21, 11, 45, 50, 25, 37, 51, 35])
In [15]:
# Filling in missing values in the train dataset
Train['MONTANT'].fillna((Train['MONTANT'].mean()), inplace=True)
Train['FREQUENCE_RECH'].fillna((Train['FREQUENCE_RECH'].mean()), inplace=True)
#Train['REVENUE'].fillna((Train['REVENUE'].mean()), inplace=True)
Train['ARPU_SEGMENT'].fillna((Train['ARPU_SEGMENT'].mean()), inplace=True)
Train['FREQUENCE'].fillna((Train['FREQUENCE'].mean()), inplace=True)
Train['DATA_VOLUME'].fillna((Train['DATA_VOLUME'].mean()), inplace=True)
Train['FREQ_TOP_PACK'].fillna((Train['FREQ_TOP_PACK'].mean()), inplace=True)
In [16]:
# Checking for null values in the Train dataset
Train.isnull().sum()
Out[16]:
user_id           0
TENURE            0
MONTANT           0
FREQUENCE_RECH    0
ARPU_SEGMENT      0
FREQUENCE         0
DATA_VOLUME       0
REGULARITY        0
FREQ_TOP_PACK     0
CHURN             0
dtype: int64
In [17]:
# Filling in missing values
Test['MONTANT'].fillna((Test['MONTANT'].mean()), inplace=True)
Test['FREQUENCE_RECH'].fillna((Test['FREQUENCE_RECH'].mean()), inplace=True)
#Test['REVENUE'].fillna((Test['REVENUE'].mean()), inplace=True)
Test['ARPU_SEGMENT'].fillna((Test['ARPU_SEGMENT'].mean()), inplace=True)
Test['FREQUENCE'].fillna((Test['FREQUENCE'].mean()), inplace=True)
Test['DATA_VOLUME'].fillna((Test['DATA_VOLUME'].mean()), inplace=True)
Test['FREQ_TOP_PACK'].fillna((Test['FREQ_TOP_PACK'].mean()), inplace=True)

Checking for Outliers in some columns¶

In [18]:
sns.set(rc = {'figure.figsize':(10,5)})
sns.boxplot(Train['ARPU_SEGMENT'])

/Users/Admin/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[18]:
<AxesSubplot:xlabel='ARPU_SEGMENT'>
In [19]:
sns.set(rc = {'figure.figsize':(10,5)})
sns.boxplot(Train['REGULARITY'])

/Users/Admin/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[19]:
<AxesSubplot:xlabel='REGULARITY'>
In [20]:
sns.set(rc = {'figure.figsize':(10,5)})
sns.boxplot(Train['ARPU_SEGMENT'])

/Users/Admin/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[20]:
<AxesSubplot:xlabel='ARPU_SEGMENT'>

Treating Outliers¶

In [21]:
Train[['ARPU_SEGMENT']].describe()
Out[21]:
ARPU_SEGMENT
count 1.077024e+06
mean 1.835356e+03
std 1.948397e+03
min 0.000000e+00
25% 6.330000e+02
50% 1.835356e+03
75% 1.835356e+03
max 1.773920e+05
In [22]:
Train['ARPU_SEGMENT'].quantile(0.25)
Out[22]:
633.0
In [23]:
Train['ARPU_SEGMENT'].quantile(0.75)
Out[23]:
1835.3559605915466
In [24]:
Q1 = Train['ARPU_SEGMENT'].quantile(0.25)
Q3 = Train['ARPU_SEGMENT'].quantile(0.75)
IQR = Q3 - Q1
In [25]:
IQR
Out[25]:
1202.3559605915466
In [26]:
lower_lim = Q1 - 1.5* IQR
upper_lim = Q3 + 1.5* IQR
In [27]:
lower_lim
Out[27]:
-1170.53394088732
In [28]:
upper_lim
Out[28]:
3638.8899014788667
In [29]:
outliers_15_low = (Train['ARPU_SEGMENT']< lower_lim)
In [30]:
outliers_15_up = (Train['ARPU_SEGMENT']> upper_lim)
In [31]:
len(Train['ARPU_SEGMENT']) - (len(Train['ARPU_SEGMENT'][outliers_15_low] ) + len(Train['ARPU_SEGMENT'][outliers_15_up] ))
Out[31]:
970967
In [32]:
Train['ARPU_SEGMENT'][(outliers_15_low |outliers_15_up)]
Out[32]:
0           7201.0
4           4117.0
18         24093.0
35          4333.0
47          4690.0
            ...   
1076979     4366.0
1076989     4364.0
1077011     8830.0
1077013     4233.0
1077016     5396.0
Name: ARPU_SEGMENT, Length: 106057, dtype: float64
In [33]:
Train['ARPU_SEGMENT'][~(outliers_15_low |outliers_15_up)]
Out[33]:
1          1835.355961
2          1835.355961
3          2632.000000
5           667.000000
6          1000.000000
              ...     
1077019    1835.355961
1077020     833.000000
1077021    1835.355961
1077022     200.000000
1077023     500.000000
Name: ARPU_SEGMENT, Length: 970967, dtype: float64
In [34]:
Train
Out[34]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
0 7ee9e11e342e27c70455960acc80d3f91c1286d1 K > 24 month 20000.000000 47.000000 7201.000000 52.000000 8835.000000 62 30.000000 0
1 50443f42bdc92b10388fc56e520e4421a5fa655c K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 0
2 da90b5c1a9b204c186079f89969aa01cb03c91b2 K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 0
3 364ec1b424cdc64c25441a444a16930289a0051e K > 24 month 7900.000000 19.000000 2632.000000 25.000000 9385.000000 61 7.000000 0
4 d5a5247005bc6d41d3d99f4ef312ebb5f640f2cb K > 24 month 12350.000000 21.000000 4117.000000 29.000000 9360.000000 56 11.000000 0
... ... ... ... ... ... ... ... ... ... ...
1077019 56e22fe3312a48cf860b043a021dab275383a20a K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 16 9.262446 0
1077020 587b72930e4d36b3fb94a18c3ef65c2079460eee K > 24 month 2500.000000 5.000000 833.000000 5.000000 0.000000 34 2.000000 0
1077021 d6831b8edacb7d9928b3f053fb1283574577ae42 K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 1
1077022 cb1f6510b084173c0deae49095f35ac29f916701 K > 24 month 600.000000 1.000000 200.000000 1.000000 591.000000 16 1.000000 0
1077023 28f56fd0d9f8d8647bb6c62e7a3f4f35f49f4d6f K > 24 month 1500.000000 4.000000 500.000000 5.000000 1265.000000 50 2.000000 0

1077024 rows × 10 columns

In [35]:
Train = Train[~(outliers_15_low |outliers_15_up)]
In [36]:
Train
Out[36]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
1 50443f42bdc92b10388fc56e520e4421a5fa655c K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 0
2 da90b5c1a9b204c186079f89969aa01cb03c91b2 K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 0
3 364ec1b424cdc64c25441a444a16930289a0051e K > 24 month 7900.000000 19.000000 2632.000000 25.000000 9385.000000 61 7.000000 0
5 b91a81e590d0dd029d99002d4226b9ca2cdf3cbc E 6-9 month 2000.000000 2.000000 667.000000 3.000000 4206.000000 62 1.000000 0
6 42335a2a6a2a4cd30452ce9bc611bd66b3a9d4c2 K > 24 month 3000.000000 1.000000 1000.000000 1.000000 3368.801722 6 1.000000 0
... ... ... ... ... ... ... ... ... ... ...
1077019 56e22fe3312a48cf860b043a021dab275383a20a K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 16 9.262446 0
1077020 587b72930e4d36b3fb94a18c3ef65c2079460eee K > 24 month 2500.000000 5.000000 833.000000 5.000000 0.000000 34 2.000000 0
1077021 d6831b8edacb7d9928b3f053fb1283574577ae42 K > 24 month 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 1
1077022 cb1f6510b084173c0deae49095f35ac29f916701 K > 24 month 600.000000 1.000000 200.000000 1.000000 591.000000 16 1.000000 0
1077023 28f56fd0d9f8d8647bb6c62e7a3f4f35f49f4d6f K > 24 month 1500.000000 4.000000 500.000000 5.000000 1265.000000 50 2.000000 0

970967 rows × 10 columns

In [37]:
sns.boxplot(x = Train['ARPU_SEGMENT'])
Out[37]:
<AxesSubplot:xlabel='ARPU_SEGMENT'>
In [38]:
#Checking if predictor class is balanced.
print(Train['CHURN'].value_counts())
plt.figure(figsize=(10,5))
Train['CHURN'].value_counts(normalize=True).plot(kind='bar')
plt.ylabel('counts')
plt.xlabel('Churn')

0    770163
1    200804
Name: CHURN, dtype: int64
Out[38]:
Text(0.5, 0, 'Churn')

We have an imbalanced predictor class. I will use the Oversampling smote balancing method later in my feature engineering to balance the data before modeling¶

In [39]:
# Checking the correlation
Train.corr()
Out[39]:
MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
MONTANT 1.000000 0.730147 0.846969 0.664758 0.169163 0.091168 0.526761 0.145531
FREQUENCE_RECH 0.730147 1.000000 0.713643 0.884401 0.107192 0.179278 0.758561 0.078075
ARPU_SEGMENT 0.846969 0.713643 1.000000 0.766184 0.185431 0.116494 0.551149 0.143347
FREQUENCE 0.664758 0.884401 0.766184 1.000000 0.123771 0.230550 0.723220 0.051828
DATA_VOLUME 0.169163 0.107192 0.185431 0.123771 1.000000 0.057755 0.085620 0.026293
REGULARITY 0.091168 0.179278 0.116494 0.230550 0.057755 1.000000 0.039928 -0.469006
FREQ_TOP_PACK 0.526761 0.758561 0.551149 0.723220 0.085620 0.039928 1.000000 0.105240
CHURN 0.145531 0.078075 0.143347 0.051828 0.026293 -0.469006 0.105240 1.000000
In [40]:
# Heat map of correlation. 
sns.set(rc = {'figure.figsize':(9,9)})
sns.heatmap(Train.corr(), annot =True)
Out[40]:
<AxesSubplot:>
In [41]:
#Train['CHURN']= Train['CHURN'].replace(0,'Not Churned')
#Train['CHURN']= Train['CHURN'].replace(1,'Churned')
#Train

Visualisations¶

Q1. Are customers with longer tenures with the company less likely to churn¶

In [42]:
# plotting the counts of unique values of the Tenure column.
print(Train['TENURE'].value_counts())
plt.figure(figsize=(10,5))
Train['TENURE'].value_counts(normalize=True).plot(kind='bar')
plt.ylabel('counts')
plt.xlabel('TENURE')

K > 24 month     919907
I 18-21 month     20779
H 15-18 month     12033
G 12-15 month      7039
J 21-24 month      5764
F 9-12 month       4235
E 6-9 month         829
D 3-6 month         381
Name: TENURE, dtype: int64
Out[42]:
Text(0.5, 0, 'TENURE')
In [43]:
sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Train, x= 'TENURE', hue = 'CHURN', palette = 'inferno_r')
Out[43]:
<AxesSubplot:xlabel='TENURE', ylabel='count'>

¶

In [44]:
# Visualizing the relationship with Tenure and churn. 
sns.set(rc = {'figure.figsize': (10,6)})
sns.countplot(data = Train, x= 'TENURE', hue = 'CHURN', palette = 'twilight')
Out[44]:
<AxesSubplot:xlabel='TENURE', ylabel='count'>

Customers with longer tenures with a company are generally less likely to churn compared to new customers. This is because longer-tenured customers have already established a relationship with the company, are familiar with its products and services, and have likely experienced positive interactions with the company over time. This is evidently shown in the visualization above.¶

Q3. Do customers who earn more income churn less than those who earn less?¶

In [45]:
sns.histplot(data=Train, x="ARPU_SEGMENT", hue = 'CHURN', bins = 10)
Out[45]:
<AxesSubplot:xlabel='ARPU_SEGMENT', ylabel='Count'>

Q3. Do customers with more active days churn less than those less active days?¶

In [46]:
sns.histplot(data=Train, x="REGULARITY", hue = 'CHURN', bins = 10)
Out[46]:
<AxesSubplot:xlabel='REGULARITY', ylabel='Count'>
In [47]:
#sns.set(rc = {'figure.figsize': (20,10)})
#sns.displot(Train, x="REGULARITY", hue = 'CHURN' , bins=60)

Feature Engineering¶

In [48]:
# checking for unique vales for dataset 
Train.nunique()
Out[48]:
user_id           970967
TENURE                 8
MONTANT             2336
FREQUENCE_RECH        84
ARPU_SEGMENT        3640
FREQUENCE             91
DATA_VOLUME        21150
REGULARITY            62
FREQ_TOP_PACK         98
CHURN                  2
dtype: int64
In [49]:
# Stating the categorical data
cat_cols = Train.select_dtypes(include=["object"]).columns.to_list()
cat_cols
Out[49]:
['user_id', 'TENURE']
In [50]:
# Dropping the ID column 
Train.drop(columns = ['user_id'], inplace = True)

/var/folders/xr/z3nw6rbs7k5g_8qmkl696c4w0000gp/T/ipykernel_73051/694804483.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Train.drop(columns = ['user_id'], inplace = True)
In [51]:
# Applying label encoding to the Tenure column
le = LabelEncoder()
Train['TENURE'] = le.fit_transform(Train['TENURE'])

/var/folders/xr/z3nw6rbs7k5g_8qmkl696c4w0000gp/T/ipykernel_73051/3162494724.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Train['TENURE'] = le.fit_transform(Train['TENURE'])
In [52]:
Train
Out[52]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
1 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 0
2 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 0
3 7 7900.000000 19.000000 2632.000000 25.000000 9385.000000 61 7.000000 0
5 1 2000.000000 2.000000 667.000000 3.000000 4206.000000 62 1.000000 0
6 7 3000.000000 1.000000 1000.000000 1.000000 3368.801722 6 1.000000 0
... ... ... ... ... ... ... ... ... ...
1077019 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 16 9.262446 0
1077020 7 2500.000000 5.000000 833.000000 5.000000 0.000000 34 2.000000 0
1077021 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 3 9.262446 1
1077022 7 600.000000 1.000000 200.000000 1.000000 591.000000 16 1.000000 0
1077023 7 1500.000000 4.000000 500.000000 5.000000 1265.000000 50 2.000000 0

970967 rows × 9 columns

In [53]:
Test
Out[53]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 I 18-21 month 2500.000000 5.00000 833.000000 5.00000 0.000000 35 5.000000
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e K > 24 month 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
2 5a4db591c953a8d8f373877fad37aaf4268899a1 K > 24 month 5506.547759 11.48394 1828.750281 13.93967 0.000000 22 9.234326
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c K > 24 month 5506.547759 11.48394 1828.750281 13.93967 3358.421151 6 9.234326
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df K > 24 month 5100.000000 7.00000 1879.000000 15.00000 7783.000000 60 4.000000
... ... ... ... ... ... ... ... ... ...
190058 1092956a3dad77ceb7e8d7c70e3e13f77b60e2aa F 9-12 month 500.000000 1.00000 169.000000 1.00000 0.000000 14 1.000000
190059 bec10becca7faa8e9cab9981b3aee5e9a7f04828 K > 24 month 2000.000000 4.00000 667.000000 5.00000 0.000000 29 3.000000
190060 7ac6fc191f8732b1b146e57f9ede983626b93eae K > 24 month 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
190061 d592c81971d6120b0d19f9ace85f278ea21b89a0 K > 24 month 300.000000 2.00000 99.000000 2.00000 0.000000 12 9.234326
190062 9b4f57aeef72cd52c634498d0ea27ea3fbb67bf4 K > 24 month 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326

190063 rows × 9 columns

In [54]:
# Applying label encoding to the Tenure column
le = LabelEncoder()
Test['TENURE'] = le.fit_transform(Test['TENURE'])
In [55]:
Test
Out[55]:
user_id TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 5 2500.000000 5.00000 833.000000 5.00000 0.000000 35 5.000000
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
2 5a4db591c953a8d8f373877fad37aaf4268899a1 7 5506.547759 11.48394 1828.750281 13.93967 0.000000 22 9.234326
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 6 9.234326
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df 7 5100.000000 7.00000 1879.000000 15.00000 7783.000000 60 4.000000
... ... ... ... ... ... ... ... ... ...
190058 1092956a3dad77ceb7e8d7c70e3e13f77b60e2aa 2 500.000000 1.00000 169.000000 1.00000 0.000000 14 1.000000
190059 bec10becca7faa8e9cab9981b3aee5e9a7f04828 7 2000.000000 4.00000 667.000000 5.00000 0.000000 29 3.000000
190060 7ac6fc191f8732b1b146e57f9ede983626b93eae 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
190061 d592c81971d6120b0d19f9ace85f278ea21b89a0 7 300.000000 2.00000 99.000000 2.00000 0.000000 12 9.234326
190062 9b4f57aeef72cd52c634498d0ea27ea3fbb67bf4 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326

190063 rows × 9 columns

In [56]:
# Dropping the ID column
Test.drop(columns =['user_id'], inplace = True)
Test
Out[56]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 5 2500.000000 5.00000 833.000000 5.00000 0.000000 35 5.000000
1 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
2 7 5506.547759 11.48394 1828.750281 13.93967 0.000000 22 9.234326
3 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 6 9.234326
4 7 5100.000000 7.00000 1879.000000 15.00000 7783.000000 60 4.000000
... ... ... ... ... ... ... ... ...
190058 2 500.000000 1.00000 169.000000 1.00000 0.000000 14 1.000000
190059 7 2000.000000 4.00000 667.000000 5.00000 0.000000 29 3.000000
190060 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
190061 7 300.000000 2.00000 99.000000 2.00000 0.000000 12 9.234326
190062 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326

190063 rows × 8 columns

Splitting Data for Modeling¶

In [57]:
# Splitting data
# Defining the target & predictor variables
X = Train.drop(columns = ["CHURN"])
y = Train["CHURN"]

# Splitting the dataframe into train and test
X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.25, random_state = 1, stratify = y)
In [58]:
# Joining train dataset for modelling 
train_data = X_train.join(y_train, on = X_train.index)
train_data.head()
Out[58]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
850510 7 9100.000000 12.000000 2223.000000 16.000000 3368.801722 60 5.000000 0
638215 5 600.000000 2.000000 200.000000 2.000000 229.000000 23 1.000000 0
635165 7 1000.000000 2.000000 333.000000 2.000000 1.000000 61 2.000000 0
194282 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 1
631158 7 1600.000000 4.000000 534.000000 6.000000 1216.000000 22 1.000000 0
In [59]:
train_data.shape
Out[59]:
(728225, 9)
In [60]:
# Joining test dataset for modelling
test_data = X_test.join(y_test, on = X_test.index)
test_data.head()
Out[60]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
733032 7 3000.000000 4.000000 739.000000 7.000000 0.000000 22 1.000000 0
404607 7 9500.000000 15.000000 3144.000000 15.000000 34383.000000 60 8.000000 0
723217 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 1
858083 7 3000.000000 4.000000 933.000000 6.000000 2374.000000 41 2.000000 0
82115 7 5529.210895 11.523756 1835.355961 13.974439 3368.801722 1 9.262446 1
In [61]:
test_data.shape
Out[61]:
(242742, 9)
In [62]:
# Applying a count to know the number of churned and not churned customers 
count_not_churned, count_churned = train_data['CHURN'].value_counts()
count_not_churned, count_churned
Out[62]:
(577622, 150603)
In [63]:
count_churned
Out[63]:
150603
In [64]:
count_not_churned
Out[64]:
577622

As stated earlier, we have an imbalanced data, hence I am going to use the Over Sampling Smote balancing method to balance the dataset for training and evaluation.¶

Over Sampling SMOTE Balancing¶

In [65]:
# Performing splits to balance the data
X = train_data.drop('CHURN',axis='columns')
y = train_data['CHURN']
In [66]:
# Using the SMOTE balancing to balance data
smote = SMOTE(sampling_strategy='minority')
X_sm, y_sm = smote.fit_resample(X, y)
y_sm.value_counts()
Out[66]:
0    577622
1    577622
Name: CHURN, dtype: int64
In [67]:
# Further splitting of balanced data
X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_sm, y_sm, test_size=0.2,random_state=1, stratify=y_sm)
In [72]:
#Scaling the train_data set 
num_cols = ['MONTANT', 'FREQUENCE_RECH', 'ARPU_SEGMENT', 'FREQUENCE',
       'DATA_VOLUME','REGULARITY', 'FREQ_TOP_PACK']
scaler = MinMaxScaler()
train_data[num_cols] = scaler.fit_transform(train_data[num_cols])
In [73]:
#Scaling the test_data set 
num_cols = ['MONTANT', 'FREQUENCE_RECH', 'ARPU_SEGMENT', 'FREQUENCE',
       'DATA_VOLUME','REGULARITY', 'FREQ_TOP_PACK']
scaler = MinMaxScaler()
test_data[num_cols] = scaler.fit_transform(test_data[num_cols])
In [74]:
test_data
Out[74]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK CHURN
733032 7 0.027553 0.036585 0.203134 0.068966 0.000000 0.344262 0.000000 0
404607 7 0.087752 0.170732 0.864211 0.160920 0.037587 0.967213 0.071429 0
723217 7 0.050977 0.128338 0.504496 0.149131 0.003683 0.000000 0.084311 1
858083 7 0.027553 0.036585 0.256460 0.057471 0.002595 0.655738 0.010204 0
82115 7 0.050977 0.128338 0.504496 0.149131 0.003683 0.000000 0.084311 1
... ... ... ... ... ... ... ... ... ...
716575 7 0.009956 0.024390 0.100880 0.011494 0.003683 0.081967 0.000000 0
125423 7 0.070618 0.292683 0.723749 0.310345 0.007160 0.721311 0.214286 0
920509 7 0.000695 0.000000 0.009071 0.000000 0.000001 0.016393 0.000000 1
796938 7 0.050977 0.128338 0.504496 0.149131 0.003683 0.000000 0.084311 1
116239 4 0.103496 0.231707 0.943650 0.206897 0.000851 0.672131 0.112245 0

242742 rows × 9 columns

In [75]:
Test
Out[75]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 5 2500.000000 5.00000 833.000000 5.00000 0.000000 35 5.000000
1 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
2 7 5506.547759 11.48394 1828.750281 13.93967 0.000000 22 9.234326
3 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 6 9.234326
4 7 5100.000000 7.00000 1879.000000 15.00000 7783.000000 60 4.000000
... ... ... ... ... ... ... ... ...
190058 2 500.000000 1.00000 169.000000 1.00000 0.000000 14 1.000000
190059 7 2000.000000 4.00000 667.000000 5.00000 0.000000 29 3.000000
190060 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326
190061 7 300.000000 2.00000 99.000000 2.00000 0.000000 12 9.234326
190062 7 5506.547759 11.48394 1828.750281 13.93967 3358.421151 2 9.234326

190063 rows × 8 columns

In [76]:
Test[num_cols] = scaler.fit_transform(Test[num_cols])
In [77]:
Test
Out[77]:
TENURE MONTANT FREQUENCE_RECH ARPU_SEGMENT FREQUENCE DATA_VOLUME REGULARITY FREQ_TOP_PACK
0 5 0.012284 0.033613 0.014093 0.044444 0.000000 0.557377 0.010283
1 7 0.027207 0.088100 0.030940 0.143774 0.004449 0.016393 0.021168
2 7 0.027207 0.088100 0.030940 0.143774 0.000000 0.344262 0.021168
3 7 0.027207 0.088100 0.030940 0.143774 0.004449 0.081967 0.021168
4 7 0.025189 0.050420 0.031790 0.155556 0.010311 0.967213 0.007712
... ... ... ... ... ... ... ... ...
190058 2 0.002358 0.000000 0.002859 0.000000 0.000000 0.213115 0.000000
190059 7 0.009803 0.025210 0.011285 0.044444 0.000000 0.459016 0.005141
190060 7 0.027207 0.088100 0.030940 0.143774 0.004449 0.016393 0.021168
190061 7 0.001365 0.008403 0.001675 0.011111 0.000000 0.180328 0.021168
190062 7 0.027207 0.088100 0.030940 0.143774 0.004449 0.016393 0.021168

190063 rows × 8 columns

Training four(4) models, then I'd select the best performing model and further tune it¶

Logistic Regression Model¶

In [78]:
# Logistic Regression
log_reg = LogisticRegression()
log_reg_model = log_reg.fit(X_train_sm, y_train_sm)
warnings.filterwarnings('ignore')
In [79]:
## Feature Importance of the Random Forest Model
log_reg_importance = log_reg_model.coef_[0]
log_reg_importance = pd.DataFrame(log_reg_importance, index = X.columns)
log_reg_importance.reset_index(inplace = True)
log_reg_importance.rename(columns = {"index": "Feature", 0:"Score"}, inplace = True)
log_reg_importance.sort_values(by = "Score", ascending = False, inplace = True)
log_reg_importance
Out[79]:
Feature Score
0 TENURE 0.045332
7 FREQ_TOP_PACK 0.020871
2 FREQUENCE_RECH 0.010269
3 ARPU_SEGMENT 0.000360
1 MONTANT 0.000095
5 DATA_VOLUME 0.000039
4 FREQUENCE -0.007249
6 REGULARITY -0.110106
In [80]:
y_pred = log_reg.predict(X_test_sm)
y_true = y_test_sm
In [81]:
# Confusion Matrix for Logistic regression model 
cm_lr = confusion_matrix(y_true,y_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_lr, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('y_pred')
plt.ylabel = ('y_true')
plt.show()
In [82]:
# Evaluating the model
smote_report = classification_report(y_true, y_pred, target_names= ["Stayed", "Churned"])
print(smote_report)

              precision    recall  f1-score   support

      Stayed       0.86      0.75      0.80    115524
     Churned       0.78      0.87      0.82    115525

    accuracy                           0.81    231049
   macro avg       0.82      0.81      0.81    231049
weighted avg       0.82      0.81      0.81    231049
In [83]:
# Finding the F2 Score
log_reg_score = fbeta_score(y_true, y_pred, average='binary', beta= 0.9)
In [84]:
# Calculate and show the AUC and ROC
fpr, tpr, thresholds = roc_curve(y_true, y_pred)
plt.plot(fpr, tpr)
#plt.xlabel("False Positive Rate")
#plt.ylabel("True Positive Rate")
plt.show()
print(f"{log_reg} AUC score: {roc_auc_score(y_true, y_pred)}")

LogisticRegression() AUC score: 0.8130004440086855
In [85]:
log_reg_auc = roc_auc_score(y_true, y_pred)
log_reg_auc
Out[85]:
0.8130004440086855

Random Forest with Smote Balance¶

In [86]:
# Specifying the Model name and setting default parameters 
rfc = RandomForestClassifier(random_state= 24)
rfc = rfc.fit(X_train_sm, y_train_sm)
In [87]:
# Feature Importance of the Random Forest Model
rf_importance = rfc.feature_importances_
rf_importance = pd.DataFrame(rf_importance, columns = ["score"]).reset_index()
rf_importance["Feature"] = list(X.columns)
rf_importance.drop(columns = ["index"], inplace = True)
rf_importance.sort_values(by = "score", ascending = False, ignore_index = True, inplace = True)
In [88]:
## Visualizing the feature importances
fig = px.bar(rf_importance, x = "Feature", y = "score")
fig.show()
In [89]:
rfc_pred = rfc.predict(X_test_sm)
rfc_true = y_test_sm
In [90]:
# Confusion matrix for RandomForest classifier
cm_rfc = confusion_matrix(rfc_true,rfc_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_rfc, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('rfc_pred')
plt.ylabel = ('rfc_true')
plt.show()
In [91]:
# Evaluating the model
smote_report = classification_report(rfc_true, rfc_pred, target_names= ["Stayed", "Churned"])
print(smote_report)

              precision    recall  f1-score   support

      Stayed       0.89      0.80      0.84    115524
     Churned       0.82      0.91      0.86    115525

    accuracy                           0.85    231049
   macro avg       0.86      0.85      0.85    231049
weighted avg       0.86      0.85      0.85    231049
In [92]:
rfc_score = fbeta_score(rfc_true, rfc_pred, average='binary', beta= 0.9)
In [93]:
# Calculate and show the AUC and ROC
fpr, tpr, thresholds = roc_curve(rfc_true, rfc_pred)
plt.plot(fpr, tpr)
#plt.xlabel("False Positive Rate")
#plt.ylabel("True Positive Rate")
plt.show()
print(f"{rfc} AUC score: {roc_auc_score(rfc_true, rfc_pred)}")

RandomForestClassifier(random_state=24) AUC score: 0.852836180464006
In [94]:
rfc_auc = roc_auc_score(rfc_true, rfc_pred)
rfc_auc
Out[94]:
0.852836180464006

The accuracy score for the RandomForest Classifier is 0.87. This Model also has an F1 and F2 score of 0.86 and 0.87 respectively. The random forest classifier gives a better accuracy as compared to the Logistic regression¶

Decision Tree Classifier with Smote Balance¶

In [95]:
# Specifying the Model name and setting default parameters 
dt = DecisionTreeClassifier(random_state=100)
dt = dt.fit(X_train_sm, y_train_sm)
In [96]:
dt_pred = dt.predict(X_test_sm)
dt_true = y_test_sm
In [97]:
#Confusion Matrix for Decision Tree Classifier 
cm_dt = confusion_matrix(dt_true,dt_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_dt, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('dt_pred')
plt.ylabel = ('dt_true')
plt.show()
In [98]:
# Evaluating the model
smote_report = classification_report(dt_true, dt_pred, target_names= ["Stayed", "Churned"])
print(smote_report)

              precision    recall  f1-score   support

      Stayed       0.89      0.77      0.83    115524
     Churned       0.80      0.90      0.85    115525

    accuracy                           0.84    231049
   macro avg       0.84      0.84      0.84    231049
weighted avg       0.84      0.84      0.84    231049
In [99]:
dt_score = fbeta_score(dt_true, dt_pred, average='binary', beta= 0.9)
In [100]:
# Calculate and show the AUC and ROC
fpr, tpr, thresholds = roc_curve(dt_true, dt_pred)
plt.plot(fpr, tpr)
#plt.xlabel("False Positive Rate")
#plt.ylabel("True Positive Rate")
plt.show()
print(f"{dt} AUC score: {roc_auc_score(dt_true, dt_pred)}")

DecisionTreeClassifier(random_state=100) AUC score: 0.8373026305639508
In [101]:
dt_auc = roc_auc_score(dt_true, dt_pred)
dt_auc
Out[101]:
0.8373026305639508

K-Nearest Neighbours¶

In [102]:
# Specifying the Model name and setting default parameters 
knn = KNeighborsClassifier(n_neighbors = 15)
knn = knn.fit(X_train_sm, y_train_sm)
In [103]:
knn_pred = knn.predict(X_test_sm)
knn_true = y_test_sm
In [104]:
# Confusion Matrix for KNN
cm_knn = confusion_matrix(knn_true,knn_pred)
f, ax = plt.subplots(figsize=(8,8))
sns.heatmap(cm_knn, annot=True, linewidth=0.5, fmt=".0f",cmap='RdPu',ax = ax)
plt.xlabel = ('knn_pred')
plt.ylabel = ('knn_true')
plt.show()
In [105]:
# Evaluating the model
smote_report = classification_report(knn_true, knn_pred, target_names= ["Stayed", "Churned"])
print(smote_report)

              precision    recall  f1-score   support

      Stayed       0.86      0.77      0.81    115524
     Churned       0.79      0.87      0.83    115525

    accuracy                           0.82    231049
   macro avg       0.82      0.82      0.82    231049
weighted avg       0.82      0.82      0.82    231049
In [106]:
knn_score = fbeta_score(knn_true, knn_pred, average='binary', beta= 0.9)
In [107]:
# Calculating the AUC and ROC
fpr, tpr, thresholds = roc_curve(knn_true, knn_pred)
plt.plot(fpr, tpr)
plt.show()
print(f"{knn} AUC score: {roc_auc_score(knn_true, knn_pred)}")

KNeighborsClassifier(n_neighbors=15) AUC score: 0.8206698597497671
In [108]:
knn_auc = roc_auc_score(knn_true, knn_pred)
knn_auc
Out[108]:
0.8206698597497671

Comparing the trained models with their various AUC scores¶

In [109]:
# Comparing Model scores and select the best for hyperparameter tuning
model_scores = pd.DataFrame (\
                             {"Models":[str(log_reg),str(rfc),str(knn),str(dt)],
                              "AUC score":[log_reg_auc, rfc_auc,knn_auc, dt_auc]})
model_scores
Out[109]:
Models AUC score
0 LogisticRegression() 0.813000
1 RandomForestClassifier(random_state=24) 0.852836
2 KNeighborsClassifier(n_neighbors=15) 0.820670
3 DecisionTreeClassifier(random_state=100) 0.837303

From the table above, we can see that the RandomForestClassifier has a better score as compared to the other trained models, hence, my best model is the RandomForestClassifier.¶

Hyperparameter Tuning & Cross Validation for RandomForest Model¶

In [110]:
rand = RandomForestClassifier(bootstrap=True,criterion = "gini",
                              n_jobs=-1,
                              max_depth=15,
                              n_estimators=500,
                              random_state= 1,
                              min_samples_leaf = 1, 
                              min_samples_split = 2,
                              verbose=True)
In [111]:
#Fit model on data
randmodel = rand.fit(X_train_sm,y_train_sm)

[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   11.0s
[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   59.8s
[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  2.5min
[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  2.8min finished
In [112]:
randpred = randmodel.predict(X_test_sm)

[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.4s
[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:    2.0s
[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:    4.4s
[Parallel(n_jobs=8)]: Done 500 out of 500 | elapsed:    5.1s finished
In [113]:
print("Accuracy")
accuracy_score(y_test_sm, randpred)

Accuracy
Out[113]:
0.8425615345662608
In [114]:
print("Recall")
recall_score(y_test_sm, randpred)

Recall
Out[114]:
0.8886301666305995
In [115]:
print("F1 Score")
f1_score(y_test_sm, randpred)

F1 Score
Out[115]:
0.849495643251384
In [116]:
# Evaluating the model
best_grid_rf_report = classification_report(y_test_sm, randpred, target_names=["Stayed", "Churned"])
print(best_grid_rf_report)

              precision    recall  f1-score   support

      Stayed       0.88      0.80      0.83    115524
     Churned       0.81      0.89      0.85    115525

    accuracy                           0.84    231049
   macro avg       0.85      0.84      0.84    231049
weighted avg       0.85      0.84      0.84    231049
In [117]:
# Calculate and show the AUC and ROC
fpr, tpr, thresholds = roc_curve(y_test_sm, randpred)
plt.plot(fpr, tpr)
plt.show()
print(f"{randmodel} AUC score: {roc_auc_score(y_test_sm, randpred)}")

RandomForestClassifier(max_depth=15, n_estimators=500, n_jobs=-1,
                       random_state=1, verbose=True) AUC score: 0.8425613351763849
In [118]:
subpred = randmodel.predict(Test)

[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.1s
[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:    1.2s
[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:    2.3s
[Parallel(n_jobs=8)]: Done 500 out of 500 | elapsed:    2.6s finished
In [119]:
subpred
Out[119]:
array([0, 0, 0, ..., 0, 0, 0])
In [120]:
submission = pd.read_csv('/Users/Admin/Desktop/Churn Capstone/SampleSubmission.csv')
In [121]:
submission.head()
Out[121]:
user_id CHURN
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 0
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e 0
2 5a4db591c953a8d8f373877fad37aaf4268899a1 0
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c 0
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df 0
In [122]:
submission["CHURN"] = subpred
In [123]:
submission.head()
Out[123]:
user_id CHURN
0 51fe4c3347db1f8571d18ac03f716c41acee30a4 0
1 5ad5d67c175bce107cc97b98c4e37dcc38aa7f3e 0
2 5a4db591c953a8d8f373877fad37aaf4268899a1 0
3 8bf9b4d8880aeba1c9a0da48be78f12e629be37c 0
4 c7cdf2af01e9fa95bf498b68c122aa4b9a8d10df 0
In [124]:
submission.to_csv('Sample_submission.csv', index=False)

Exportation¶

In [125]:
requirements = '\n'.join(f'{m.__name__.replace("_","-").replace("sklearn","scikit-learn")}=={m.__version__}' for m in globals().values() if getattr(m, '__version__', None))

with open('requirements.txt', 'w') as f:
    f.write(requirements)

print(requirements)
     

pandas==1.4.2
numpy==1.21.5
seaborn==0.11.2
pandas-profiling==3.4.0

Objects to export¶

In [126]:
to_export = {
    "encoder": le,
    "scaler": scaler,
    "model": randmodel,
    "pipeline": None,
}

Loading after exportation¶

In [127]:
import pickle
In [128]:
with open('ML_items', 'wb') as file:
    Loaded_object = pickle.dump(to_export, file)
In [129]:
git clone https://huggingface.co/spaces/SampsonChris/Customer_churn_gradio

  Input In [129]
    git clone https://huggingface.co/spaces/SampsonChris/Customer_churn_gradio
        ^
SyntaxError: invalid syntax
In [ ]: